STATISTICAL PAPERS
Series
C No. 1, Rev.2
Recommendations
for the Preparation
of
Sample Survey Reports
(Provisional
Issue)
UNITED
NATIONS
DEPARTMENT OF ECONOMIC AND SOCIAL AFFAIRS
STATISTJCAL
STATlsTJCAL
OFFICE
OF THE
UNITED
NATIONS
STATISTICAL PAPERS
i
i
seriesC
Series
No. 1 v Rev.2
,,.
Recommendations
for the Preparation
’
Sample
SuWby
,
(Provisional
UNITED
.
Reports
Issue)
NATIONS
New York,
1964
of
NOTE
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TABLE
OFCONTENTS
Page
Foreword
. . . . . . . . ..*..............
Set tion
I. Introduction
II.
Preliminary
. . . . . . . . . . . . .. . . . . ..
Report
III.
General Report
IV.
Technical
V.
VI.
VII.
V
...............
..................
Report.
................
Design of the Survey ...............
Methods of Selecting
Statistical
Analysis
the Sample.
7
. . . . . .
12
. . . . . . . . . . . . . . .
14
0
FOREWORD
The first
document on sampling terminology
entitled The Preparation
of
Sampling Survey Reports was prepared by the Sub-Commission
on Statistical
Sampling of the Statistical
Commission
at its second session,
30 August to
11 September
1948. In its report to the. Statistical
Commission
the SubCommission
stated:
“The Sub-Commission
considered that if accounts of sampling investigations included the points enumerated,
and if they dealt with the technical
aspects of sampling processes
in accordance with the recommended terminology, it would become increasingly
possible to improve sampling practices
in many important respects.
The Sub-Commission
believed that the wide cir
culation of reports,
prepared generally in accordance with the suggestions
contained in the memorandum,
will foster international
exchange of experience, and will suggest the use of sampling in various undertakings
now being
carried out by complete counts or by non-random partial surveys.”
(Document E/CN.3/52.)
The document was revised by the Sub-Commission
at its third session, 12 to
23 September 1949.
The members of the Sub-Commission
were: Mr. G. Darmois,
Mr. W. E.
Deming, Mr. P. C. Mahalanobis (Chairman),
Mr. F. Yates and Mr. R. A. Fisher
(Consultant).
The Statistical
Commission,
at its twelfth session held in 1962, considered
that with the passage of time, development in many methodological
aspects of
sampling had rendered
the Sub-Commission
report somewhat out of date. It
therefore
recommended
that the Secretary-General
appoint a group of experts
to undertake
the preparation
of a revised set of recommendations
on sampling
terminology,
to be issued by the Secretary-General
to supersede the document.
Tn pursuance
of this resolution
a group of, sampling experts was convened in
Geneva from 1 to 12 July 1963.
The Group elected Mr. F. Yates as Chairman and Mr. P. C. Mahalanobis as
Vice-Chairman.
The other members of the Group were: Mr. T. Dalenius, Mr.N.
Keyfitz, Mr. V. Monakhov, and Mr..P. Thionet.. Mr. S. Zarkovic
participated
in
the meetings as representative
of the Food and Agriculture
Organisation
of the
United Nations, Mr. D. B. Lahiri of the ‘Indian Statistical
Institute
was present
as an observer.
Mr. P. J. Loftus, Director of the Statistical Office, represented
the Secretary-General.and
Mr. M. D. Palekar served as secretary
of the Group.
The present
document
contains
the recommendations
V
of the Expert
Group.
I.. INTRODUCTION
These recommendations
have been prepared in the hope that they will be of
assistance
to those engaged inpreparingreportsonsample
surveys. They necessarily involve technical terminology,
the use of which will foster clarity, comprehensiveness,
and international
comparability
in reports that deal with aims,
methods used, and accuracy attained. Information
suppliedon the lines suggested
will tenable those making use of the reports to utilize the results obtained to full
advantage, to assess for themselves
the reliability of these results, and to utilize
the experience
gained in the conduct of the survey in planning future surveys of
similar
nature. The recommendations
are not intended to be final, but are to be
regarded as an outline of the various points which require description
and analysis in the application of sampling methods in various socio-economic
fields. It
may be mentioned that many of the recommendations
are applicable also to censuses and other complete enumerations.
Three types of survey
reports
may be distinguished:
(i) preliminary,
(ii1 general, and (iii) technical;
Sections II to IV deal respectively
with the nature and content of each type of report. SectionV gives a brief description
of the
various types of sampling commonly employed, together with definitions of the
technical terms used. Section VI gives a similar description of methods of selecting the sample. Section VII contains definitions and explanations of terms used
in statistical
analysis.
[I.
PRELIMINARY
REPORT
A preliminary
report is often required to make available data of current
interest
as rapidly as possible; such results
may relate to selected important
characters
sometimes based on a sub-sample
of the full sample. It should contain a brief statement concerning the survey methods and the limitations
of the
data. As a very minimum, information
should be given concerning
the size of
the sample, the method of selecting the sample and discrepancies
observed between external and internal data. Fuller details can be given in the general and
technical reports on the survey.
2
III.
GENERAL
REPORT
This should contain a general description of the survey for the use of those who are primarily
interested
in the results
rather than in the technical statistical
aspects of the sample design, execution and analysis,
The general description
should include information on the following points :
1. Statement of purposes
of the survey: A general indication should be given of the purposes of the
survey and “margin of error”
which .would be permissible
for the purposes of the survey, and the
ways in which it is expected that the results will be utilized. In this connexion it is useful to distinguish the following types of surveys:
(il
Integrated survey:
In an integrated survey, data on several subjects (or items, or topics) are
collected for the same set of sampling units for studyingtherelationshipamongitems
belonging
to different subject fields. Suchsurveys
areof special importance in studies on levels of living.
Integrated surveys
of consumption and productive enterprises
are also of special importance
in developing countries where the related activities
are frequently undertaken in an integrated
manner in the household.
(ii)
Multi-subject
survey: When in a singlesurveyoperationseveral
subjects, not necessarily
very
closely related, are simultaneously
investigated for the sake of economy and convenience, the
investigation
may be called a multi-subject
survey.
The data on different subjects need not
necessarily
be obtained for the same set of sampling units, or even for the same type of units
(e.g., households, fields, schools, etc.).
(iii)
ContinuinP surveys: The most usual example .of these surveys occurs where a permanent sampling staff conducts a series of repetitive
surveys which frequently include questions on the
same topics in order to provide continuous series deemed of special importance to a country.
Questions on the continued topics can frequently be supplemented by questions on other topics,
depending upon the needs of the country.
Ad hoc survey: This is a survey without any plan for repetition.
(iv)
(v)
Multi-purpose survey: This term is sometimes used in connexion with sampling organizations
which conduct surveys in various fields of interest to several departments or parties, keeping
in view their diverse purposes. This permits economies in the technical and other resources
and their more effective use, particularly in developing countries.
(vi)
Specialized or special-purpose survey: This may be defined as an investigation focussing on
a single set of objectives which, because of their nature or complexity, requires specialized
knowledge or the use of special equipment by a technical staff with training in the subject field
of enquiry.
2. Description of the coverage: An exactdescriptionshouldbe
given of the geographic region or branch
of the economy or social group or other categories of constituent parts of a population covered by
the survey. In a survey of a human population, for example, it is necessary to specify whether such
categories as hotel residents, institutions (e.g., boarding houses, sanatoria), persons without fixed
abode, military personnel, were included and to indicate the order of magnitude of the categories
omitted. The reporter should guard against any possible misapprehension regarding the coverage
of the survey.
3. Collection of information: The nature of the information collected should be reported in considerable
detail, including a statement of items of information collected but not reported upon. The inclusion
of copies of the questionnaire or other schedules, and relevant parts of the instructions used in the
survey (including special rules for coding and classifying) is of great value, and such documents
should therefore be reproduced in the report if possible.
The information may be collected by direct investigation, or by mail or telephone. Direct investigation may involve objective methods of observation or measurement. The method of collection
should be reported, together with the nature of steps taken to ensure that the information is as
complete as possible (e.g., methods of dealing with non-response). The extent and causes of nonresponse; etc. should be stated.
It is of importance to describe the type and number of investigators, e.g., whether whole or
part-time, permanent or temporary, with particulars of their training and qualifications.
4. Repetition: It is important to state whether the survey is an isolated one or is oneof a series of
similar surveys. Where the survey is repetitive and some of the sampling units reappear in the
successive stages, this should be stated.
5. Numerical results: A general indication should be given of the methods followed in the derivation
of the numerical results. Particulars should be givenof methods of weighting and of any supplemen3
tary information
for non-response
utilized, for example
should be described.
to obtain
ratio
estimates.
Any special methods of allowing
6. Date and duration: There are two periods of time which are important for any survey: (i) the period
to which data refer, or reference
period, (ii) period of collection, that is, the period taken for the
field work.
In order to minimize memory lapses, the length of the reference period is sometimes fixed but
not the end points, as in “preceding weeknquestionq
(the collection period being comparatively
long).
In such cases, the reference period may be called moving in contrast with a fixed reference period
when the end points arefixed. The reference period, if properly selected, enhances the use and value
of the information collected in the survey. Sometimes different reference periods are used for different topics in the same survey.
This is done with a view to eliciting more accurate information
from the respondents,
as in a familybudget survey, where questions on food items may be asked for
the preceding week or month, but information on clothing or furniture or some durable goods may
be asked for the whole year. In health surveys, different reference periods are used for diffe,rent
items, e.g., illness during the preceding two weeks, hospitalization
during the year. When the field
work is conducted on a continuous and successive
basis, the parts of the survey which are operationally separate are called rounds.
7.
Accuracy:
A general indication of the accuracy
made between .sampling errors and non-sampling
attained should be given and a distinction
errors.
should be
8. Cost: An indication should be given of the cost of the survey, under such headings as preliminary
work, field investigations,
analysis, etc. Resources used in the conduct of the survey but not included
in the costs should be stated.
9.
10.
Assessment:
The extent to which the purposes of the survey were fulfilled should be assessed.
Responsibility:
The names of the organizations sponsoring and conducting the survey should be stated.
11. References:
References
should be given to any available
4
reports
or papers
relating
to the survey.
IV.
TECHNICAL
REPORT
Technical reports should be issued for surveys of particular
importance and those using new techniques
and procedures
of special interest.
In addition to covering such fundamental points as the purposes of the
survey, conditions to be fulfilled and resources
available for the survey, the report should deal in detail
with technical statistical
aspects of the sampling design, execution and analysis; the operational and other
special aspects should also be fully covered.
1. Specification
of the frame: A detailed account of the specification
of the frame should be given: this
should define the geographic areas and categories of material included and the date and source of
the frame. If the frame has been emendedor constructed
ab initio the method of emendation or construction should be described.
Particulars
should be given of any kno.vn or suspected deficiencies,
2.
Design of the survey:
The sampling design should be carefully specified, including details such as
types of sampling unit, sampling fractions,
particulars
of stratification,
etc. The procedure used in
selecting sampling units should be described andif it is not by random selection the reporter
should
indicate the evidence on which he relies for adopting an alternative procedure.
3. Personnel and equipment: It is desirable to give an account of the organization
ployed in collecting, processing
and tabulating the primary data, together with
their previous training and experience. Arrangements
for training, inspection
staff should be explained; as also should methods of checking the accuracy
the point of collection. A brief mentionof the equipment used is frequently of
report.
of the personnel eminformation regarding
and supervision
of the
of the primary data at
value to readers of the
4. Statistical
analysis and computational
procedure:
The statistical
methods followed in the compilation of the final summary tables from the primary data should be described. If anv more elaborate
processes
of estimation than simple totals and means have been used, the methods followed should
be explained, the relevant formulae being reproduced where necessary.
It frequently
happens that quantities of which estimates are required do not correspond
exactly
to those observed:
in a crop-cutting
survey, for example, the yields of the sample plots give estimates of the amount of grain, etc., in the standing crop, whereas the final yields will be affected by
losses at various stages, such as harvesting,
storing, transport,
marketing,
etc. In such cases adjustments
may have to be made, the amount of which is estimated by subsidiary
observations,
or
otherwise.
An account should be given ‘of the nature of these adjustments
and the ways in which they
were’derived.
The steps taken to ensure the elimination of
sample checks, etc.) and to ensure the accuracy
in detail. Mention should be made of the methods
lation, etc.) including methods used for the control
gross errors from the primary data (by scrutiny,
of the subsequent calculations
should be indicated
of processing
the data (punched cards, hand tabuof errors.
In recent years electronic computers have been applied to the making of estimates from sample
surveys.
In addition, they have been programmed
to tasks of editing with consequent improvement
of consistency
of the primary
data and possibly of its accuracy.
Their use goes beyond the mere
speeding up of existing methods and lowering cost, They have made possrble some forms of estimation (e.g., the fitting of constants in analysis of variance with unequal numbers in the sub-classes)
on a scale which would have been impracticable
with hand methods. In some instances regression
on previous surveys of a series with overlapping samples can substantially
improve the precision
of estimates.
The use of these computers
makes possible changes in the allocation of resources
between the collection of the data and the processing.
The amount of tabular matter included in the report, and the extent to which the results are discussed, will depend on the purposes of the report. If a critical statistical
analysis of the results embodied in the final summary tables has been made, it is important that the methods followed should
be fully described. Numerical examples are often of assistance
in making the procedure clear. Mention should be made of further tabulations which have been prepared but are not included in the report, and also of critical statistical
analyses which failed to yield results of interest and which are
therefore not considered to be worth reporting in detail.
The inclusion of additional numerical information
which is not of immediate relevance to the
report .but which will enable subsequent workers
to carry out critical statistical
analyses which appear to them to beof interest shouldbe carefully considered. If, for example, in addition to the class
means of each main classificationof
the data, the sub-class
numbers (but not the means) of the various two-way
classifications
are reported, a study of the effects of each of the main classifications
freed from the effects of all other classifioations
can be made (provided the effects are additive)
without further reference to the original information.
5
5. Accuracy
of the survey:
(1) Precision’
as indicated by the random sampling errors
deducible from the survey: Standard
deviations
of sampling units should be given in addition to such standard errors (of means,
totals, etc.) as are of interest.
The process of deducing these estimates of error should be
made entirely clear. This process will depend intimately on the design of the sample survey.
An analysis of the varianoes of the sampling units into such components as appears to be of
interest for the planning of future surveys, is also of great value.
(ii)
Degree of agreement observed between independent investigators
covering the same material:
Suoh comparison will be possible only when interpenetrating
samples have been used, or when
checks have been imposed on part of the survey. It is only by these means that the survey can
provide an objeotive test of possible personal equations (differential
bias among the investigators). Any such comparisons
or checks should be fully reported.
(iii)
Other non-sampling
errors:
The existence and possible effects of non-sampling
errors on the
accuracy of the results, andof incompleteness
inthe recorded information
(e.g., non-response,
lack of records,
whether covering the whole of the survey or particular
areas or categories
of the material), should be fully discussed.
Any special checks instituted to control and determine the magnitude of these errors should be described, and the results reported.
Another source of erroris
that due to incorrect
determinations
of the adjustments
(referrred
to in Section VII-4) arising from observation
of quantities which do not correspond
exactly to the quantities of which estimates are required.
6. Accuracy,
completeness
and adequacy of the frame: The accuracy of the frame can and should be
checked and corrected
automatically
in the course of the enquiry, and such checks afford useful
guidance for the future. Its completeness
and adequacy cannot be judged by internal evidence alone.
Thus, complete omission of a geographic region or the complete or partial omission of any particular class of the material intended to be covered cannot be discovered by the enquiry itself and
auxiliary
investigations
have often to be made. These should be put on record, indicating the extent
of inaccuracy which may be ascribable to such defects.
7. Comparisons
with other sources of information:
Every reasonable effort should be made to provide
comparisons
with other independent sources of information.
Such comparisons
should be reported
along with the other results,
and the significant differences
should be discussed.
The object of this
is not to throw light on the sampling error, since a well designed survey provides adequate internal
estimates of such errors,
but rather to gain knowledge of biases, and other non-random errors.
Disagreement
between results of a sample survey and other independeni sources may of course
sometimes be due, in whole or in part, to differences
in concepts and definitions or to errors in the
information from other sources.
8. Costing analysis : The sampling method can often supply the required information
with greater speed
and at lower cost than a complete enumeration.
For this reason, information on the costs involved
in sample surveys are of particular
value for the development of sample surveys within a country
and are also of help to other countries.
It is therefore recommended that fairly detailed information
should be given on costs of. sample
surveys.
Costing information
should be given under such headings as planning (showing separately
the cost of pilot studies), field work, supervision,
processing,
analysis and overhead costs. In addition, labour costs in man-weeks
of different grades of staff and also time required for interviewing,
travel and transport
costs should be given. The collection of such Information
is often worthwhile,
since it may suggest methods of economizingin the planning of future surveys. Moreover,
the preparation of an efficient design involves a knowledge of the various components of costs as well as of
the components
of variance. It should be emphasized that the concept of cost in this respect should
be regarded broadly in the sense of economic cost and should therefore take account of indirect costs
which may not have been charged administratively
to the survey. Wherever possible, the costing data
should distinguish the time and resources
devoted to the various operations involved in the survey.
9. Efficiency:
The results
of a survey often provide information which enables investigations
to be
made on the efficiency of the sampling designs, in relation to other sampling designs which might have
been used in the survey, The results of any such investigations
should be reported. To be fully relevant the relative costs of the different sampling methods must be taken into account when assessing
the relative efficiency of different designs and intensities of sampling.
Such an investigation
can be extended to consideration
of the relation between the cost of carrying out surveys of different levels of accuracy and the losses resulting from errors in the estimates
provided.
This provides a basis for determining
whether the survey was fully adequate for its purpose, or whether future surveys should be planned to give results of higher or lower accuracy.
10. Observations
of technicians:
The critical observations
of technicians in regard to the survey,
part of if, should be given, These observations
will help others to improve their operations;
6
or any
V.
DESIGN OF THE SURVEY
1. Elementary units: These are the smallest
characteristics
which are to be observed
types may therefore
occur in the same
of children is acharacteristicof
afamily,
prise individuals.
Individual elementary
sometimes
called recording units.
physical units which are capable of possessing one or more
or measured in the survey. Elementary units of different
survey, and one type may comprise another. Thus, number
age is a characteristic
of an individual: and families comunits or groups of elementary units used for recording are
2. Sampling units (or sample units): These are the units which form the basis of the process of sampling from the aggregate of elementary
units intended to be covered in the survey. The sampling
unit may be (i) the same as the elementary unit, or (ii) a group of elementary units. A group may
consist of contiguous elementary units or of units arranged in an assigned configuration.
A systematic pattern of elementary units may, for instance, constitute a sampling unit.
1
It is important that the sampling units be so defined
correctly
and unambiguously.
For example, in enquiries
unit may be a family of which a possible definition would
lated by blood or marriage,
and having a common family
that it is possible to identify them clearly,
concerningthe
level of living, the sampling
be a group of persons living together, rebudget.
It is also conceptually convenient that they be so defined that the totality of sampling units contains every elementary unit once, and once only. If this condition is departed from (e.g., by the use
of circular
areas in area sampling), attention must be paid to the question of whether bias is likely
to arise owing to boundary conditions or other factors.
It is important to specify the quantitative and qualitative characteristics
of the sampling units
which are to be recorded in the enquiry, as these form the basis of all subsequent analysis, and in
the cases of multi-stage
and multi-phase
sampling, may also have been used in determining the
sampling procedure.
It may often be possible to associate a measure of size with the sampling unit. Sampling units
may be of the same or different size. They may also contain the same, or approximately
the same,
number of elementary units, or they may contain widely different numbers, Inmulti-stage
sampling,
the sampling units for each stage are sub-divisions
of the units for the previous stage.
3. Physical field (or field of interest):
Thephysicalfield
or field of interest comprises
a finite number
of elementary
units of one or more types (or in special cases a finite continuum which is divided
into arbitrary
parts which constitute elementary units for the purpose of survey) and which are located within a given boundary demarcating the field intended to be covered. Thus, the boundaries of
the physical field may be the same as those of a country or geographic area, and the elementary
units may be human beings, families, schools, villages, farms, fields, enterprises,
etc., “lying within the specified boundaries”.
4,
Frame : The frame ccnsists of previously available descriptions
of the objects or material related to
the physical field in the form of maps, lists, directories,
etc., from which sampling units may be
constructed
and a set of sampling units selected: and also information
on communications,
transport, etc., which may be of value in improving the design for the choice of sampling units, and in
the formation of strata, etc. Supplementary information
available for the field covered by the frame
may also be of value in improving the accuracy of estimates obtained from the survey. Frames that
are initially available often require emendation, particularly
in the later stages of multi-stage
sampling, before they may be considered adequate, and even then may be incomplete: at times a frame ’
may require to be constructed
ab inftfo. In repeated surveys information
collected in the earlier
surveys may often serve to improve the frame for later surveys.
5.
Field of enquiry* The entire field of enquiry is co-extensive
,with the frame used as its basis,
frame is incomplete the field of enquiry will not coincide exactly with the field of interest.’
6.
Population (or universe or agpregate):L’
The term population is used in several senses which must
be clearly distinguished.
In addition to its common usage, e.g., the human population of a country,
it is used in statistical
terminology
to denote an aggregate of elementary or sampling units, finite
in number, or an aggregate of numerical quantities or qualities appertaining to these units, such as
the heights or sexes of the individuals of a human population, not all of which may be known, but all
of which’ are conceptually
determinable,
or to denote numerical quantities which are generated by
the process of random sampling from a defined distribution,
and which are conceptually infinite in
number, e.g., random sample values from a normal distribution.
If the
I/ English sampling terminology
has taken terms like “population”
from common usage and given them a technical meaning. The preservation of this identity with common usage is not nece:sary
in other Imguages..
Suitable words, if necessary
with different meanings, should,
therefore,
be used in translations
to make the technical meaning clear in ~a& case in each language.
7
In sample surveys the term population is restricted to the following uses:
(i) When there is one type of elementary unit, as equivalent to the field of interest or enquiry.
(The term universe is sometimes also used). Whenthere are two or more types of elementary
units, these would be regarded as constituting two or more populations.
(ii)
Occasionally, to denote the totality of sampling units of a given type constructed from the elementary units.
(iii)
To. denote the totality of numerical quantities of a particular type relating to individual elementary units or sampling units of a given type. These numerical quantities may be subject to
errors of measurement; in this case there is associated with each unit a conceptually infinite
population of measurements, of which the actual measurement constitutes a sample of one
value (or several values if the measurement is replicated).
7. Domain of study (or sub-population): Any sub-division of the field of enquiry about which the survey
is used to supply information maybe termed a domain of study. The sub-divisions may coincide with
those used for stratification, or they may differ. It is desirable to indicate the smallest domains of
study about which the enquiry may be expected to provide information of adequate accuracy. It should
be noted that stratification will not greatly reduce the sampling errors of domains of study that cut
across strata.
8. The sample: The aggregate of the samplingunits selected constitutes the, sample. A’random or probability sample supplies both objective estimates (of means, totals, etc.) and, if certain conditions
are satisfied, information for estimating their precision. The word sample may be used either for
the aggregate of sampling units chosen from a single stratum or at a single stage, or in a wider
sense, for the whole of the sampling units’chosen in a complete enquiry.
9.
Simple random sample (or unrestricted random sample, or fully random sample): A simple random
sample is one in which the sampling units are independently selected with equal probability, the
sampling being “without replacement” and without any restrictions. This is equivalent, for a sample
of n units from a population of N units, to the selection with equal probability of one out of all pcssible combinations of n out of Nunits. The term random sample (without qualification) is sometimes
used to denote a simple random sample.
10. Sampling with probability proportional to size: In certain cases it may be advantageous, or convenient, to select the sampling units from .the whole population or from within strata with probabilities proportional to some known quantitative characteristic of the units, such as size, or indeed
some function of this quantity,. e.g,, the square root; if more than one unit is to be selected from the
population (or from individual strata) exact probabilities proportional to size can only be simply
attained if the sampling is “with replatiement”, units selected twice or more being counted twice or
more.2
The methods of calculating the population estimates appropriate to samples selected with probability proportional to size differ from thosd for samples selected with uniform probability.
The totality of sampling units included in the frame may be divided into groups or
11. Stratification:
strata, each stratum being sampledseparately and independently, so that a specified number of sampling units or a specific fraction of all such units is obtained from each stratum. Such strata may
be geographic sub-divisions, branches of the economy, or other divisions dependent on some quantitative or qualitative variate appertaining to the sampling units, etc.
12.
Uniform and variable sampling fractions: The numbers of sampling units specified may be such that
from each stratum the same fraction of unit is selected,. in which dase the term stratification with
uniforkn sampling fraction or proportionate sampling, is used, or the numbers specified may be such
that different fractions of the different strata are selected, in which case the term stratification with
variable sampling fraction is used. In this sense, the sample is usually a specified fraction of the
stratum it represents.
13. Multiple stratification: In certain c&es the totality of sampling units may be divided simultaneously
according to two or more classifications, each of which depends on one quantitative or qualitative
variate. Each cell determined by the two or more way classification, itself potentially constitutes a
stratum from the totality of samplingunits. If each of the cells is sampled separately, as in an ordinary stratified sample, the term multiple stratification maybe used without qualification. Thus, one
may stratify farms accordingto size and accordingto geographic regions. If the farms in each region
are classified into size groups before taking the sample, the’n the region-size-group combinations
form the individual sub-strata. If the available information is not adequate for this to be done, SO
that the numbers of sampling units in the main strata only can be pre-determined! this has been
termed multiple stratification without control of sub-strata, but is better termed marginal stratification. In these circumstances, however, the selection of a sample presents both theoretical and
practical difficulties, and a calculation of the sampling error is also troublesome.
14. Balanced sample: If the average valueof somecharacter to be used for purposes of control is known
for units that make up the whole population, it is possible, provided the value of the character is
u A method of overcoming
W. G. Co&ran
in ‘On a Simple
Series 6, Vol. 24, No. 2 (1962).
the difficulties
that arise in sampling without replacement
is described
by J. N. K. Rae. H.O. Hartley,
and
Procedure
of Unequal Probabiliry
Sampling Without Replacement.’
Journal of the Royal Statistical
Society,
pp. 482991.
8
known for
character
population.
fied sample
15.
each sampling uniti to select a sample in such a manner that the average value of this
for the units selected approximates,
within an assigned margin, to the average for the
Provided the numbers of units selectedfromthe
individual strata are adequate, a stratimay be separately balanced for each stratum.
Multi-stage
sampling: In multi-stage
(two-stage,
three-stage,
etc.) sampling, the frame is first divided into sampling units, referred
to as first-stage
units (or primary sampling units). A 8 ple of
St *or
such units is selected. Next, each such unit selected is further divided into second-stage
secondary
sampling units). A sample of such units is then selected from each first-stage
unit selected. ‘Ibis procedure may be extended to include three, four, etc., stages.
In multi-stage
sampling a complete frame of first-stage
units is required: for each first-stage
unit selected, a frame of second-stage
units is required, etc. One of the advantages of multi-stage
sampling is that second-stage
frames are required only for the first-stage
units selected, and so
on. When the frame of the last stage is constructed
in the field the last-stage
units are sometimes
termed listing units.
For example, a country may be considered as divided into a number of districts:
each district
into a number of villages : each village into a number of farms. In multi-stage
sampling a number of
districts., is selected in the first stage: within each such selected district
a number of villages is
selected in the second stage, and from each selected village a number of farms is selected in the
third stage for enquiry. In the case of a crop-cutting
investigation,
the work may be carried further
by the selection of fields.a from each selected farm and by plots within a field.
The devices of stratification
and a variable samplingfraction
may be used at any stage. If a variable sampling fraction at the first stage is used, the sampling fractions at the later stages may be
determined
so as tc give a uniform over-all sampling fraction. If the selection is with probability
proportional
to size at the first stage (see Section V-lo), the sampling fractions
at the later stages
may be similarly
determined to give equal probability of selection to all elementary units.
16. Controlled sampling: This term is definedtomeanany.process
of selection in which, whilb rhihining the assigned probability
for each, unit, the probabilities
of selection for some or all preferred
combination of n out of N units are larger than in stratified
random sampling (and correspondingly
the probabilities
of selection
for at least ,some non-preferred
combinations
are smaller than in
stratified
random sampling).
Controlled sampling is mainly practicable and useful when the sample is made up of a few large
first-stage
units. It then enables additional controltobe
introduced beyond what is possible by stratification alone, this being limited by the fact that the number of strata cannot exceed the number of
units in the sample. No reliable estimate ‘of the first-stage
sampling error is possible for a controlled sample3
17. Multi-phase
sampling: It is’sometimes
convenient and economical to collect certain items of information on the whole of the units of a sample, and other items of information”oii
only some of these
units, these latter units being so chosen as to constitute a sub-sample
of the units of the original
sample. This is termed two-phase
sampling. Information collected at the second or sub-sampling
phase may be collected at a later time, and in this event, information obtained on all relevant units
of the first-phase
sample may be utilized, if this appears advantageous,
in the selection of the
second-phase
sample. Further phases may be added as required.
An important
application of multi-phase
sampling
first phase as supplementary
information
to provide
of regression
or ratios),
of the means, totals, etc.,
the second phase.
Information obtained in a complete census may be
obtained from a sample, in which case the complete
and second phase sample respectively.
is the use of the information obtained at the
more accurate estimates (e.g., by the method
of variates on which data are obtained only in
used in this manner to improve the estimates
census and sample are analogous to the first
Multi-phase
sampling may be combined with multi-stage
sampling. In a scheme for the estimation of the areas and yields of anagricultural
crop, for example, a two-stage
sample of villages and
farms may be taken for the estimation of areas, and a sub-sample of these farms may be taken for
the estimation of yields, This is two-phase at the second stage.
An extension of multi-phase
sampling is the use of information obtained from first-phase
units
for the construction
of new sampling units made up of groups of the first-phase
units. These are
then used as a basis for one-or-more-stage
sampling.
18. Interpenetrating
(networks
of) samples: Whatever be the basic design of the survey (with or without
stratification:
single- or multi-stage;
single- or multi-phase),
it is possible if desired to arrange the
sampling units in sets of two or more interpenetrating
networks
of units, each of which constitutes
a sub-sample,
and to collect and process the information for each such sub-sample
in an independent
manner so that each sub-sample
would supply an independent estimate of the population values of
the variates under study, In some cases the interpenetrating
sub-samples
may be regarded as analou Details are given by Roe Goodman and Leslie Kish in Yontrolled
Selection:
American
Statistical
Association,
Vol. 45, No. 249 (March,
1950). pp. 350-372.
9
A Technique
in
Probability
Sampling.’
Journal
of the
gues of plots in the theory of design of experiments,
and an analysis of variance
in the,usual way. Another term for this procedure is replicated sampling.
can be carried
out
It is possible to keep some of the sampling units the same in two (or more) interpenetrating
networks of samples so that information
for such sampling units is independently collected twice (or
more than two times). It is then possible to make detailed comparisons
between the two (or more)
sets of observations.
Such sampling units which are observed more than once are called duplicated
(triplicated,
quadruplicated,
etc.) sampling units.
Interpenetrating
samples can be used to secure estimates of sampling errors
on non-sampling
errors
such as differences
arising from differential
interviewer
methods of eliciting information,
etc.
and information
bias, different
The interpenetrating
network therefore provides a means of control (i.e., appraisal) of the quality
of the information.
For example, in a family budget enquiry, in each domain of study the sampling
units may be chosen in the form of two or more independent sets each of which covers the whole
domain, and the information for every such set may be collected by a different investigator.
In this
way, an independent estimate for the whole domain would be obtained based on the information
collected by each investigator,
and a comparison
of such independent estimates (for example by an
analysis of variance)
would show whether there were significant differences
between different investigators
which may often indicate appropriate action for adjustments of the results of the survey
or for improving the planning of similar surveys in the future.
The contrasts
between the estimates provided by the sub-samples
of an interpenetrating
network
can be used to provide estimates of the sampling error for these estimates.
These estimates of error
include any contributions
due to coding and computational errors and those due to differential
bias
between observers
(and also differential
bias in coding if separate coders are allocated to the different sub-samples).
In a stratified
sample independent contrasts
will be available from each stratum, and these will often provide a basis for reasonably reliable estimates of the error of the whole
sample. Similar
estimates
of error
(but excluding observer
bias) can in many cases be obtained
from a sample which was not planned as an interpenetrating
network by sub-dividing
the sample into
sub-samples
in some appropriate
random manner.
19. Probability
sampling: The selection of sampling units (at a given stage) is said to be carried out by
probability sampling, if each sampling unit has a known, non-zero, probability of being selected. The
term embraces many very different types of sampling, e.g., random sampling of all kinds, systematic sampling with random starting point, and controlled sampling, and is therefore unsuitable for
use in reports
of surveys without specification
of the actual type, except when the purpose is to
emphasize that methods like purposive sampling are not being used.
20.
Cluster
sampling: This’is a general term which includes single-stage
sampling with sampling units
formed of groups or clusters of elementary units (usually of compact form to facilitate the collection of information),
and two-or-more-stage
sampling, in which the units at one stage are clusters
of elementary
units, these clusters being sub-sampled
at the next stage. The report should specify
clearly the sampling procedure used since the term embraces many different procedures.
21. Sub-sampling:
This term has been used in several senses : (i) to denote the selection of, for example;
second-stage
units from a selected first-stage
unit: (ii) to denote the selection, for example, at the
second phase of two-phase sampling: and (iii) to denote the selection of the individual samples that
constitute an interpenetrating
network of samples.
The term
context.
is a loose one which
should not be used except where
its meaning is clear from the
22.
Area sampling: This term refers to the use of areas as sampling units. The areas may be administrative
units, or naturally bounded areas (such as “segments”,
“blocks”,
etc.), or especially demarcated areas (such as circular
or rectangular
plots used in crop estimation surveys).
The areas
observed may, therefore,
be selected by reference to a map (which constitutes
the frame) or determined in the course of the field operation. The areas may be directly observed as in land use surveys, or they may be used todefineand observe the group of units (e.g., households) associated with
the respective
areas.
23. Line sampling: A method of sampling a geographic area using a line to select sampling units at any
stage. Parallel lines are taken, either equally (systematically)
spaced, or randomly located within
blocks.
The lines may be directly observed as in forest surveys, or intercepts
may be measured to
give proportions
of the area falling in different categories.
24.
Point sampling: A method of sampling a geographic area, using; at any stage, points located at random or systematically,
to select the sampling units. The points, or small areas demarcated with
reference
to them, may themselves constitute sampling units, as in forest surveys, or they may be
used to select sampling units, e.g., farms, with previously
determined boundaries, a unit being selected if a point falls within it, in which case probability
of selection is proportional
to the area of
the unit. Simple counts of the pointsfallingin
different categories will give estimates of the proportions of the area falling in these categories,
25.
Selection of the sample by place of work: This term is used in the USSR in sample surveys of the
level of living of families of any given social group. First, enterprises,
institutions,
and organiza10
tions are selected: next, within the selected enterprises,
institutions
and organisations,
workers
are selected and finally, for the families of the selected workers,
record is kept of such items as
incomes,
expenditures,
food consumption,
acquisition
of goods, housing, education,
and other
characteristics.
26.
Composite sampling schemes: There are occasions on wfhich different methods of sampling are required for different parts of the material. In sampling a human population, for instance, some form
of sampling of areas (in which the samplingunit
is a small area) may be most suitable for the rural
parts of the country whereas some form of sampling based on lists of households may be best in
the towns.
27.
Successive
(or repeated) surveys:
Often a sample survey of the same kind is repeated at suitable
intervals to constitute a series of successive surveys, or a continuing survey with successive
rounds.
In the USSR successive
surveys repeated over a long period on a uniform plan are called systematic
surveys.
In successive
surveys it is possible to choose entirely independent sampling units in each
survey, or to keep a certain number (or a certain proportion)
of sampling units the same in two or
more surveys,
e.g., one third of the units may be replaced in each survey, each unit then being retained for three surveys.
This latter procedure is particularly
useful for the study of changes when
there is a high correlation,
for individual sampling units, between successive
periods. However, it
should be recognised that retention of the same units for a long series of surveys presents practical
and theoretical
difficulties
since the frame is changing; repeated surveys on the same units may
also lead to atypical changes in these units. The continuing operation of a survey agency on successive surveys
of the same type usually also leads to a gradual improvement
in the quality of the
information.
28.
Longitudinal surveys:
This term is used, especially in sociological
and medical investigations,
to
denote a survey in which a sample of individuals (or other units) is observed at intervals
over a
period of time, so as to study their development as individuals.
It may be contrasted with a crosssectional survey,
in vrhich a sample of individuals in various stages of development, e.g., children
of different ages, is observed at one time. A cross-sectional
survey only furnishes information
on
changes in average values with age, etc. The sudden spurt of growth that occurs in a child at puberty,
for example, will be masked in such a survey, since puberty occurs at different ages in different
children,
29.
Pilot and exploratory
surveys:
In undertaking large scale surveys, particularly
of unexplored material, it is usually advisable to conduct pilot and exploratory
surveys to test and improve field procedure, and schedules,
and to train field workers:
also to obtain information which will enable the
sampling design to be planned more efficiently,
and to obtain an estimate of the cost. For example,
the results of a pilot survey may be used to estimate the first- and second-stage
components of variance relevant to some two-stage
sampling process which is envisaged for the main survey and also
the relevant components of cost, from which it is possible to determine the optimum intensity of
sampling at each of the two stages.
A distinction
ought to be drawn between pilot surveys and exploratory
surveys.
must necessarily
be on a random or probability
sample basis, whereas exploratory
not necessarily
be so.
11
Pilot surveys
surveys need
VI.
METHODS
OF SELECTING
THE SAMPLE
1. Random sampling: A process is properly described as random if eaoh sampling unit is independently
selected with determinate
probability.
One expeditious way of effecting a random selection is by
the use of random sampling numbers; equally, with more labour, this may be done by any of the apparatus used in games of chance.
The exact method of selection will depend on the type of sampling adopted. Thus, in a simple
random sample, the required number of units is selected “without replacement”
(i.e., no unit is selected twice) from all units in the population, each unit being independently selected with equal probability: this may be done by “drawing
lots” or any equivalent process, In a stratified
sample a selection of the requisite -number of units is made from each stratum independently, the procedure
for each stratum being the same as is used for the whole population in selecting a simple random
sample. If selection with probability proportionalto
size is required the actual selection can be made
by forming a running total of the sizes of the separate units: if the total of all units is T one can then
(using a table of random numbers) ,select numbers at random between 1 and T: a number greater
than the running total for unit s-l, and not greater than the running total for unit 8, indicates that
unit s is to be selected.
2. Selection of balanced and controlled samples and samples with marginal stratification:
A balanced
sample may be selected by using a process of replacement.
A random or stratified
random sample
of the required size is first selected, the order of selection bemg recorded, and the average value
of the known quantitative
character
is calculated for this sample. If this average is, say, greater
than the average value for the population, eachunit of the original sample which has a value greater
than the average value for the populationis taken in turn and replaced by a newly selected unit which
has a value less than the average for the population; if the new selection gives aunit withvalue greater
than average the selection is repeated until one less than average is obtained. The process is continued until an adequate degree of balance is attained.
A sample with marginal stratification
is selected by a somewhat similar but more complicated
process
using rejection instead of rep1acement.g
Selection of a controlled sample is also complicated.v
3. Systematic
sampling: A wholly or partly deterministic
procedure for the selection of a sample, in
which the selection of any particular
sampling unit in the frame depends solely upon its relative
‘place in the frame. The exact nature of the procedure will depend upon the kind of frame available.
Four examples will illustrate
this point:
(i)
(ii)
(iii)
(iv)
Systematic
sampling from a list: A systematic
sample may be selected by taking every k’th
entry. If the first entry is selected at random, the selection is termed systematic
sampling
with random starting point.
Systematic
sampling from an ordered sequence: If a list of sampling units in the frame is reordered, before the selection of the sample according to some important variate, (e.g., in a
list of workers:according
to the incomes of the individual workers)
a systematic
sample from
the ordered list may be expected to be more accurate than one of the same size selected from
the original list without ordering.
Similar considerations
apply when a sequence is naturally
ordered, e.g., in sampling a line stretching
up a hill-side in a forest.
Systematic sampling with probability proportional
to size: Instead of selecting numbers at random between 1 and T (Section VI-l) every k’th number may be taken, with, if desired, a random
starting point as in (i). The list of sampling units may be reordered as in (ii), if this is considered appropriate,
before computing the running totals.
sample may be selected by placing a regular .
Systematic
sampling from areas: A systematic
grid of points over the area, .and selecting for the sample those units (farms, etc.) on which
these points fall.
Systematic selection is often used when the person responsible
for the planning of the survey is satisfied that it is inpracticeequivalent
to a random selection in the respects required.
In such cases he accepts personal responsibility
for the judgment on which his plan is based..
4.
Purposive selection: In a purposively
selected sample one or more control variates are chosen, for
which the values are known for all sampiingunits
of the population. A sample is then selected which
matches the population in some defined manner with regard to these control variates,
ti Details are given by F. Yates in Sampling
Company, 1953.
Methods
8/ Details are given by M, H. Hansen, W. N. Hurwia,
cations
New York, John Wiley and Sons, Inc., 1953.
A
for
Censuses
and Surveys.
and W. G. Madow
12
in Sample
2d ed. revised
Survey
andenlarged.
Methods
and theory.
New York,
Hafner
Publishing
Vol. 1: Methods
and Appli-
Thus with a single control
(il
(ii)
(iii)
variate
the sample may be selected
so that:
The average of the control variate in the sample agrees approximately
with the average for
the population: if the control variate refers to some type of elementary unit contained in the
sampling units, e.g., average income of individuals,
a weighted average (with weights equal to
the numbers of elementary units in the sampling units) may be taken;
The distribution
of the control
units of the population,
Conditions
similar
variate
for the units of the sample agrees with that for all the
to (il or (ii) are satisfied
for each stratum
separately.
It is important to realize that the matching, however elaborate, of the sample and the population
with respect to one or more controlvariates
provides no guarantee that the sample is representative
for the variates under study, nor is the sample equivalent to a random sample. Nor is there any way
of estimating
the accuracy
attained. Only by‘ introducing
appropriate
random components into the
selection process can a representative
sample of ascertainable
accuracy be obtained. Thus, condition (il can be satisfied by using a balanced sample, condition (ii) by using a stratified
sample with
strata formed by grouping according to values of the control variate. For these reasons purposive
selection has ceased to be used when objective results of ascertainable
accuracy are required.
5. Quota sampling: A type of purposive selection, mostly used for opinion surveys and market research,
is known as quota sampling. Each interviewer
is given a definite quota of people to interview,
e.g.,
in different social classes, or age groups. An interviewer
may, for example, be instructed
to interview 100 people, 50 men and 50 women, 20 each of the men and women to be between 21 and 34 years
of age, the remaining 30 to be 35 years of age or older. Except for these restrictions,
he is free to
select any person for interview.
Quota sampling
6.
u
suffers
from the same defects
as other forms
of purposive
selection.
Mechanical sampling (a method used in the USSR): This is known as typical proportional
sampling
with mechanical selection of sampling units within typical groups.6,’ This method combines the elements of typical and mechanical selection. Before the selection, the population is divided into typical
groups: within each group the units of the population are arranged in a definite order according to
one of their most important characteristics;
and the sampling units are selected mechanically
so as
to eliminate the possibility
of human preferences
to affect the selection.
%@cal groups in
this
sense refer
to branches
of industry
and wage levels
and are, therefore,
domains
of study or strata.
VII. STATISTICAL
ANALYSIS
1. Estimate: This term is used technically to denote any quantity, calculated from the observed values,
which is designed to give a value representing
some numerical property of the population. Thus, in
an unrestricted
random sample the mean of the observed values provides an estimate of the mean
of the whole population; the total of the sample values divided by the proportion of units sampled
gives an estimate of the population total. Such population values are sometimes
referred to as parameters of the population, but this term is better reserved for the quantities specifying distributions,
e.g., the normal distribution,
occuring in statistical
theory.
The appropriate
method of estimation is in @art determined by the method of sampling adopted;
in part by the amount of supplementary
information
which is available and can be profitably utilized
to improve the accuracy of the estimates;
and sometimes also by the fact that more accurate estimates for comparative
purposes
are provided by biased estimates (which are usually also more
easily calculable)
for in this case the biases of the estimates to be compared are often of similar
magnitude.
For a full description
of the various methods of estimation,
appropriate,
reference must be made to the recognized text-books
and a discussion
of when they are
on sampling theory and methods,
2. Supplementary
information:
In many cases supplementary
information,
available from a complete
census or other source, can be used to increase the accuracy of the estimates,
often substantially.
In the ratio
for example, the sample is used to estimate a ratio which is then
multiplied by a known total. The estimation ,of total production of an agricultural
crop from yield
per acre is an example of this. Thedirect estimates from the total yields of the sampled fields (i.e.,
the mean of all these totals multiplied by the total number of fields in the population) would be considerably less accurate.
3. Stratification
after selection provides another example of the use of supplementary
information.
In
some cases the number of sampling units in each stratum is known from census data, but stratification is not possible because the sampling units cannot be grouped by strata before selection. If a
random sample is taken the numbers of units in the sample falling in the different strata will not
be in the same proportions
as in the population. This can be allowed for by differential
weighting.
This procedure
will give nearly the same accuracy as stratification
before selection with uniform
sampling fraction.
4. Bias in observation:
A tendency to errors in the same direction is termed a bias, or sometimes a
systematic
error. Specifically,
the bias is the amountby which the mean value will be in error when
a large number of observations
are taken. In sampling work analogous biases due to non-response
can arise when non-respondents
as a class differ in some way from respondents.
Similar biases due
to omissions
arise from incompleteness
of the frame.
5.
’
Bias of an estimate: An estimate is unbiased if the mean of its values for all possible samples selected from the population according to the specified sampling scheme is equal to the value for the
population of the quantity estimated. Apart frombiasdue
to bias in observation,
etc. absence of bias
in a mean or total is in general obtained by weighting the values of the variate obtained for each
sampling unit inversely as the probability of selection of that unit. Thus, in an unrestricted
random
sample, or a stratified
random sample with uniform sampling fraction, all sampling units are given
equal weight. In a stratified
sample with variable sampling fraction weights inversely proportional
to the sampling fractions are assigned to the different strata. In sampling with probability
proportional to size each unit is weighted inversely to its size. A further example is provided by the sampling of fields for yield of a crop, the area of the crop in a district being known. If the fields are
selected with equal probability
a virtually unbiased estimate of the total production Of a district is
obtained by giving equal weight to the total production of each field, which is equivalent to weighting
the yield per acre (or hectare)
of each field by the crop area of that field and multiplying this
weighted
mean by the total crop area: if the fields have been selected by point sampling the unweighted
mean of the yields per acre is taken. (This estimate is in fact a ratio estimate, the required ratio being provided by the estimate’ of the total production of all sampled fields divided by
the total area of all sampled fields: in point sampling the probability of selection is proportional
to
the size of the field).
Biased estimates
may, in certain circumstances,
be considerably
more accurate than unbiased
estimates,
In sampling for yield of a crop, for example, the main component of variation is from
field to field and is often practically
independent of size of field, While, therefore,
weighted means
of yields per acre may be required to provide unbiased estimates of total production comparisons
of yields per acre between small geographic areas may be better made by unweighted means, as
the gain in accuracy may outweigh the biases introduced, particularly
as these biases are likely to
be similar for different areas.
14
Trivial biases in estimation
may be ignored. The ratio method of estimation,
usually slightly biased, but inmost practicalcases
this bias is of no consequence.
6. Actual errors : An’estimate will usually differ from the quantity
error of the estimate, will generally be unknown.
estimated.
for example,
This difference,
is
the actual
7. Random samplinp errors:
If the observations
(and the calculations)
are without error, the actual
errors
in the estimates
will be wholly due to the fact that a sample only of the population was observed. ‘Apart from bias introduced
in the estimation process, with a random sampling procedure
‘positive
and negative deviations inthe individual observations
will tend to balance one another. In a
sample with an adequate number of randomly selected sampling units (or groups of sampling units)
reliable estimates of the expected magnitude of the resultant errors in the estimates,
which may be
termed random sampling errors,
may be made. The methods of error estimation appropriate to different types of sampling and methods of,estimation
of the population values are described in statistical text-books.
‘If the sampling is systematic,
fully valid estimates of error cannot be made, though in certain
cases, e.g., in a systematic
sample from an alphabetically
arranged list, the sample may be judged
sufficiently
nearly equivalent to some form of restricted
or unrestricted
random sample. Some
forms of random sampling, e.g., stratifiedsamplingwithone
unit from each stratum,‘cannot
provide
fully valid estimates of error, though in many cases reasonably satisfactory
approximate
estimates
may be made.
8. Standard error:
Estimates
of variability
and sampling error of an estimate are best expressed in
terms
of standard error.
In survey work it is convenient to use “standard error”
to refer to the
error of an estimate and “standard deviation’ to refer to the variability
of a single observation
(see
item 10 below). The square of the standard error is termed the sampling variance of the estimate.
In many cases, e.g., in an unrestricted
random sample of units of approximately
equal size, the
actual errors of estimates of the populationvalues
in a series of samples will conform fairly closely
to what is known as the normal distribution,
and $f the standard error is known the probability of
an actual error of any magnitude can be determined by reference to a table of the normal distribution. Thus the actual error, positive or negative, will be less than the standard error in 68 per cent
of all samples and less than twice the standard error in 95 per cent of all samples.
9.
Limits
of sampling
error,
margin of uncertainty,
confidence limits: Instead of standard errors,
limits of error or confidence limits at someassigned
level of probability
may be given. Thus limits
of twice the standard error
correspond
to a probability level of 5 per cent i2 l/2 per cent in each
direction).
If such limits are used the level of probability
and the factor by which the standard
errors have been multiplied must be clearly stated, but the direct use of standard errors is usually
preferable.
The contrasts
between the estimates provided by the sub-samples
of an interpenetrating
network
provide estimates of sampling errors whichinclude
any contributions
due to differential
bias between
observers,
computers,
etc. If limits based on such sampling errors are calculated in the manner of
confidence limits, then these limits specify the margin of uncertainty.
.
The symbol 2 is used both in relat,ion to standard errors
for probable error): the actual use should be clearly indicated.
and limits
of error
(and, in the past,
10. Standard deviation of a single observation:
The standard deviation of a single observation
is a measure of the variability
of the sampled material when the sampling unit is the unit of observation.
The
square of the standard deviation is called the variance of a single observation.
The relevant standard
deviations for the estimation
of errors
of estimates of the population values depend on the type of
sampling, e.g., in a stratified
sample the standard deviations within strata (which may differ from
stratum
to stratum)
are required.
Information
on the various standard deviations is also required
for studies of efficiency and for planningfuture
surveys, and they can therefore be usefully reported.
Clear distinction
must be made between the standard deviation of a single observation
and the
standard error of an estimate, e.g., a mean.
The standard deviation of a single observation,
expressed
as a percentage of the mean of the
observations,
is sometimes termed the coefficient of variation (C.V.). ‘This term is sometimes used
to denote also the standard error of an estimate exnressed as a nercentase of the estimate. The
phrases “percentage
standard deviation of a single observation”
and “percentage
standard error of
,
the estimate”
are more explicit.
11. Non-random
errors : Clear distinctionmustbe
made between components of error which are included
in the estimates
of random sampling error deducible from the survey results, and those which are
not so included, Errors which are common to all investigators,
and indeed any constant component
of error (or bias) in the recorded information, (or in a stratified
sample components of error which
are constant for a particular
stratum) orbias introduced by the estimation process, and also errors
arising from incompleteness
or inaccuracy of the frame, or lack of information
for some of the units
in the sample, will not be included in the estimates of random sampling error.
Not only are non-random errors (biases) non-estimable,
except by special investigations
designed
for the purpose, but they will not for the most part be reduced by increasing the size of the sample,
15
’
whereas
random sampling errors
can be reduced by any desired amount by sufficiently
increasing
the sample size, and can often also be reduced by improving the sampling design.
12. Accuracy,
precision:
The word “accuracy”
is sometimes used to include both sampling errors and
non-sampling
errors
and “precision”
to indicate sampling errors alone. However, this use has not
been standardized
and the sense in which the words are used should therefore be made clear.
13.
Permissible
error: The error which, whenplanninga
survey, it is considered should not be exceeded
if the purposes of the survey are to be fulfilled. It is important, when a permissible
error is given
by those commissioning
the survey, to ascertain whether standard error, or limits of error at some
assigned probability
level, are meant.
14.
Fractile
graphical method of assessment
of error: A simple graphical representation
can supply a
visual (or geometric)
means of assessing and controlling errors and also a measure of the margin
of error (or of uncertainty}
especially in thecase of an interpenetrating
network of independent subsamples. The observed samplingunits
ineachsub-sample.are
ranked in ascending order of any suitable variate (or even in order of time of observation)
and the whole sub-sample
is divided into a suitable number of fractile groups of equal size (that is, consisting of observed sampling units of equal
number or of equal probability weight). Foreachsub-sample
the average value, or. median, or some
other estimate of any variate for each fractile group is plotted on paper and the successive
plotted
points are connected by straight
lines; these polygonal lines are called the fractile graphs of the
sub-samples.
A fractile graph for the whole sample canbe drawn in the same way, retaining the same number
of fractile groups, as in the case of the sub-samples.
The area between the fractile.graphs
for subsample3 provides
a visual and geometric
estimate of the error associated with the fractile graph
of the whole sample. This method can be used not .only to assess the significance of observed deviation from a statistical
hypothesis
relating to the results of the survey for any particular
part or
the whole range of observed data, but also for comparisons
between results based on two or more.
surveys carried out in different regions or at different periods of time in the same region using the
fractile graphical error for each survey. This method is being extensively used in India;5
n Details
also published
are given by P. C. Mahalanobis
in *A Method of Fracdle Graphical
in Sankhy& Series A. VoL 23, Part 1 (February,
1961). pp. 41-64.
16
Analysis.’
Econometrica:Vol.
28, No. 2 (1960).
pp. 325-367;
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